Designing a Pediatric Severe Sepsis Screening Tool
Bibliographic record
Abstract
We sought to create a screening tool with improved predictive value for pediatric severe sepsis (SS) and septic shock that can be incorporated into the electronic medical record and actively screen all patients arriving at a pediatric emergency department (ED). "Gold standard" SS cases were identified using a combination of coded discharge diagnosis and physician chart review from 7,402 children who visited a pediatric ED over 2 months. The tool's identification of SS was initially based on International Consensus Conference on Pediatric Sepsis (ICCPS) parameters that were refined by an iterative, virtual process that allowed us to propose successive changes in sepsis detection parameters in order to optimize the tool's predictive value based on receiver operating characteristics (ROC). Age-specific normal and abnormal values for heart rate (HR) and respiratory rate (RR) were empirically derived from 143,603 children seen in a second pediatric ED over 3 years. Univariate analyses were performed for each measure in the tool to assess its association with SS and to characterize it as an "early" or "late" indicator of SS. A split-sample was used to validate the final, optimized tool. The final tool incorporated age-specific thresholds for abnormal HR and RR and employed a linear temperature correction for each category. The final tool's positive predictive value was 48.7%, a significant, nearly threefold improvement over the original ICCPS tool. False positive systemic inflammatory response syndrome identifications were nearly sixfold lower.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".